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A stochastic differential equation (SDE) is a differential equation in which one or more of the terms is a stochastic process, [1] resulting in a solution which is also a stochastic process. SDEs have many applications throughout pure mathematics and are used to model various behaviours of stochastic models such as stock prices , [ 2 ] random ...
In Itô calculus, the Euler–Maruyama method (also simply called the Euler method) is a method for the approximate numerical solution of a stochastic differential equation (SDE). It is an extension of the Euler method for ordinary differential equations to stochastic differential equations named after Leonhard Euler and Gisiro Maruyama. The ...
Platen has authored and co-authored five books on numerical methods and quantitative finance. Earlier, he focused on the numerical solution of stochastic differential equations, writing three books on the topic including Numerical Solution of Stochastic Differential Equations with Peter Kloeden, Numerical Solution of SDE Through Computer Experiments with Kloeden and Henri Schurz, and Numerical ...
In mathematics of stochastic systems, the Runge–Kutta method is a technique for the approximate numerical solution of a stochastic differential equation. It is a generalisation of the Runge–Kutta method for ordinary differential equations to stochastic differential equations (SDEs). Importantly, the method does not involve knowing ...
where r is the risk-free rate, (μ, σ) are the expected return and volatility of the stock market and dB t is the increment of the Wiener process, i.e. the stochastic term of the SDE. The utility function is of the constant relative risk aversion (CRRA) form: =.
An integro-differential equation (IDE) is an equation that combines aspects of a differential equation and an integral equation. A stochastic differential equation (SDE) is an equation in which the unknown quantity is a stochastic process and the equation involves some known stochastic processes, for example, the Wiener process in the case of ...
Consider the autonomous Itō stochastic differential equation: = + with initial condition =, where denotes the Wiener process, and suppose that we wish to solve this SDE on some interval of time [,]. Then the Milstein approximation to the true solution X {\displaystyle X} is the Markov chain Y {\displaystyle Y} defined as follows:
A Langevin equation is a coarse-grained version of a more microscopic model (Risken 1996); depending on the problem in consideration, Stratonovich or Itô interpretation or even more exotic interpretations such as the isothermal interpretation, are appropriate. The Stratonovich interpretation is the most frequently used interpretation within ...